dbMap/Web – Concepts

The following outlines the PLDB system, key concepts and features.

What is PLDB?

Petrosys Prospects and Leads Database (PLDB) is a system used by geoscientists and engineers to capture, calculate and report information supporting the exploration and development of hydrocarbon resources.

Details of a prospect, target and the play data associated with the targets can be entered, and hydrocarbon resource estimates generated using a variety of calculation methods, employing stochastic methods to quantify uncertainty in the estimate.

PLDB also allows for the storage of related information about the prospects, such as maps and technical documentation.

What is PLDB used for?

PLDB can be used as a company-wide inventory of Exploration prospects and leads, and Development opportunities to exploit discovered resources, providing a consistent and methodical approach to the assessment of resource volume and risk.

It can be used to support look-back processes to ensure estimates of resource and risk are improving, as more information is acquired for an asset.

It can be used to support company portfolio processes, maintaining a record of opportunities presented and selected for budget and approval processes.

It can be used by Exploration managers to keep abreast of their inventory, and Development managers to ensure the highest value returning opportunities are selected.

Key PLDB Concepts

In understanding how PLDB works, the following covers some concepts used in PLDB.

Play Interval

A Play Interval is a stratigraphic layer, typically a formation, within a basin, that contains one or more Plays.

In PLDB, a play interval forms part of the definition of a play.

Play

A Play is defined as a mechanism to trap hydrocarbon, within a defined Play Interval.

In PLDB, plays are defined as a combination of play interval, trap type, reservoir type, and charge type. Risk is assigned at the Play level, which is used for any resource estimates for targets assigned to the play.

Target

A target is a potential accumulation of hydrocarbon.

In PLDB, a target is defined by the play type, and a name, and belongs to a prospect. Resource volumes are estimated for a target, using one of a variety of computation methods. Risk is also specified at target level, and combined with the play level risk to establish an overall geological risk for the target.

Prospect

A Prospect is a geographical location that contains one or more targets.

In PLDB, prospect resources are a roll-up of the target resources assigned to the prospect. A prospect can incorporate one or more drilling opportunities to discover yet to find resources, or appraise or exploit discovered resources.  A prospect is represented on a map by an all encompassing polygon which covers all targets' reservoir pool lateral limits.

Drilling Opportunity

A Drilling Opportunity is a well, or other project to find undiscovered or appraise and exploit discovered resources. In Exploration, a drilling opportunity is typically a wildcat well. In the Exploitation phase, drilling opportunities take the form of appraisal wells, development wells, infrastructure projects such as compression projects.

In PLDB, resource estimates can be attributed to a drilling opportunity. These resources represent the expected volume that a drilling opportunity will access or produce. As such, the resources of the drilling opportunities can be compared to the estimated resources of the target to determine if there are sufficient opportunities to fully exploit a discovered resource.

Target Resource Volumes

Resource volumes are estimated for each target using one of a variety of computation methods. With the exception of the Direct Resource method, each computation method requires the input of geological and reservoir parameters, specified as either a single or range of values for each input parameter, and determines the resulting resource volumes.

Probabilistic Distributions and Monte Carlo

In determining resource volumes, choosing single value input parameters will yield a single value output volume. This so-called 'deterministic' approach does not effectively take into account the uncertainty in these input parameters., nor the range in possible outcomes for the volume.

A 'probabilistic' approach is better suited to managing uncertainty. For this approach, a range of values is specified for each input parameter. The volume calculation is performed many times, combining these input parameters 'randomly' for each iteration. The resultant range of output volumes will reflect the true uncertainty in the resource volume. These volume outcomes are typically expressed as a mean or P50 volume, coupled with a P90 and P10 volume, to indicate of the range of outcomes.

 

To demonstrate the concept, consider a simplified Monte Carlo simulation to determine Oil Volume, using the following formula:

Oil Volume = Porosity * Hydrocarbon Saturation * Formation Volume Factor * Bulk Volume

The simulation will consist of 1000 iterations, each iteration calculating an Oil Volume, combining a different value for porosity, hydrocarbon saturation, formation volume factor and bulk volume. The values for each iteration will be selected ‘randomly’, using the distributions described by the following table.

Input Parameter	P90 value	P10 value	Distribution Type
Porosity (fraction)	0.05	0.14	Log Normal
Hydrocarbon Saturation (fraction)	0.25	0.65	Log Normal
Formation Volume Factor	1.1	1.3	Log Normal
Bulk Volume (ac.ft)	300	3000	Log Normal

The distribution ‘type’ guides the selection of random values. A Normal distribution is one where the frequency, or number of times each value is taken follows the classic bell-shape.

A Log Normal distribution is one where the frequency profile will follow a Normal distribution, if the Logarithm of the values is plotted against the frequency. Log Normal distributions are popular in describing the variation of values observed in nature, and are typically used to describe variations in input parameters for determining oil and gas volumes.

For porosity, the p90 value of 0.05 is specified, which means that for this simulation, the 1000 values randomly selected will see 90%, or 900 of the values having a porosity value of 0.05 or higher. The p10 value of 0.14 means that only 10%, or 100 iterations will see the porosity value of 0.14 or higher.

Similarly, each iteration will see a random value for hydrocarbon saturation, formation volume factor, and bulk volume selected, guided by the distribution specification for each parameter. The values for each iteration will be multiplied together to yield an Oil volume value.

Iteration Number	Porosity (fraction)	Hydrocarbon Saturation (fraction)	Formation Volume Factor	Bulk Volume (ac.ft)	Oil Volume (ac.ft)
1	0.082	0.623	1.32	410	27.648
2	0.123	0.274	1.25	2476	104.308
3	0.092	0.471	1.11	659	31.697
...
…
1000	0.091	0.341	1.12	1091	37.917

The resulting 1000 Oil Volume values will themselves form a distribution of values. This output distribution will also approximate a Log Normal distribution, by virtue of the Oil Volume values sourced from a multiplication of values.

From this output Oil Volume distribution, values for p10, p50, p90 and the Mean value can be specified.

	p99	p90	p50	Mean	p10	p1
Oil Volume (ac.ft)	2.76	9.73	39.51	66.67	143.89	470.49

Correlation

In the above example, the input parameters are selected randomly for each iteration and multiplied together to determine an Oil Volume. An iteration may result in a porosity selected from the lower end of this distribution and a hydrocarbon saturation selected from the higher end of its distribution, and vice versa. There may be evidence however that selected input parameters can be correlated, that is, as the value of one parameter increases or decreases, another parameter increase or decreases in a consistent manner.

In PLDB, a correlation value, between -1 and 1, can be specified to correlate one input parameter with another. This will influence the selection of parameter values in each iteration. If porosity and hydrocarbon saturation have a correlation factor of 1, then high values of porosity will be selected with high values of hydrocarbon saturation, and low values of porosity with low values of hydrocarbon saturation in each iteration. This will affect the output values and the overall output distribution.

The above graphs are cross-plots of porosity and hydrocarbon saturation, each value selected randomly. Each point on the graphs represent a porosity value and a hydrocarbon saturation value for an iteration. The first graph represents no (zero) correlation, the second a correlation of 0.5, and the third graph represents correlation factor of 1.

In the third graph, each value of porosity is selected with a corresponding saturation value, such that the highest porosity is selected with the highest saturation, the second highest porosity with the second highest saturation value, and so on. For a correlation value of -1, the reverse occurs, the highest porosity selected with the lowest saturation etc. A correlation factor of 0.5 will see a ‘looser pairing’ of high porosity values with high values of saturation for each iteration, but still follow the correlation factor.

Risk and Chance of Success

Risk is the measure that an event will occur, and in this sense, is the same as the chance of success.

In PLDB, a risk factor of zero means that an event will not occur, while a value of 1 ensures the event will occur.

In PLDB, risk is broken down into 2 levels, a play risk and a local target risk. Both play and local target risk comprise 'component' risk factors, including seal risk, reservoir risk and charge risk.

For an individual target, the geological risk or chance of success is simply the play risk multiplied by the local target risk.

For a roll-up of targets to a prospect, portfolio or other selection of prospects, the overall risk is determined probabilistically.

The prospect chance of success also takes into account any specified threshold economic recoverable hydrocarbon volume.

Dependency

Dependency simply means that the outcome of one event has an effect on the outcome of one or more another events.

PLDB allows for two or more targets to be linked in such a way that if one fails, the other will either be more likely to fail, or will fail.

This dependency of failure is described for one or more specific target risk factors, for example, the Seal or Charge.  

There are 4 types of dependency:

To determine the type of dependency, the shared risk is specified, and its value dictates whether the dependency is none, full or partial. The shared risk can only be a value between the highest risk value (the most likely) and 1. A shared risk of 1 indicates no dependency.

The following table illustrates shared risk and dependency of 2 targets.

Example	Play Risk	Target A Independent Risk	Target B Independent Risk	Shared Risk	Dependency Type	Outcome
1	1.0	0.7	0.5	0.7	Full	If target A fails, Target B will also fail
2	1.0	0.5	0.7	0.7	Full	If target B fails, Target A will also fail
3	1.0	0.5	0.7	0.8	Partial	If target B fails, Target A is more likely to, but not guaranteed to fail.
4	1.0	0.5	0.7	1.0	No Dependency	Targets will fail according to their independent risks.
5	1.0	0.5	0.5	0.5	Total	If Target A fails, Target B will fail, and vice versa.

Note the shared risk can only have values that lie between the highest independent risk and 1.

In example 2 above, the shared risk has the same value as that of the highest risk, defining a full dependency.  

In example 3 above, the shared risk lies between the highest independent risk and 1, and therefore defines a partial dependency.

Resource Computation Methods

PLDB offers several methods to determine resource volumes for a variety of resource types.  Each method requires input parameters, together with a distribution type, on order to determine both unrisked and risked volumes.

The following table outlines the available computation methods, for each resource type.

	Description	Conventional Oil and Gas	Coal Seam Gas	Deep Coal Gas	Tight Sands Oil and Gas	Shale Oil	Shale Gas
Gross Interval Method	Method uses estimates for porosity, hydrocarbon saturation, pool area, gross interval and a net to gross ratio to determine average net pay and ultimately a resource volume	Y
Net Pay Method	Method uses estimates for porosity, hydrocarbon saturation, pool area, and net pay to determine the resource volume.	Y
GRV Method	Method uses estimates of porosity, hydrocarbon saturation, gross rock volume and a net to gross ratio to determine a net rock volume and ultimately a resource volume.	Y
Direct Resource Method	Method allows for a direct entry of in-place raw gas and shrinkage values to determine the sales gas or recoverable oil resource volume.	Y
Depth -Area Method	Method uses depth-area pairs to approximate bulk rock volume, combined with estimates of porosity, hydrocarbon saturation, net to gross ratio, to determine a resource volume.	Y
Gas – Average Expected Ultimate Recovery	Method uses estimates for ultimate recovery per well, well spacing and pool area to determine gas resource volumes.		Y	Y			Y
CSG Area Gas Content Method	Method uses estimates for area, thickness, bulk rock density, moisture, ash and adsorbed gas content to determine gas resource volumes.		Y
Shale Gas – Ambrose 2010 Method	Method uses a combination of conventional volumetric calculations using rock volume, porosity, fluid saturations and pressure, and Langmuir Isotherms to estimate the volume of adsorbed gas, to determine original gas-in-place.						Y
Gas – Tight Sands Method	Method uses estimates for area, thickness, effective porosity, rock density and fluid saturations to determine in-place gas resources.				Y
Oil – Pyrolysis Method	Method uses estimates for area, thickness, rock density and pyrolysis S1 data to determine an oil in place estimate.					Y
Shale Oil Volume Saturation Method	Method uses estimates of area, thickness, bulk rock density, effective porosity and oil saturation to determine oil in place resource volumes.					Y

Roll-ups

Roll-ups are a key feature of PLDB, when an understanding of the total value of a prospect, portfolio, play or selection of prospects is required. Roll-ups are performed probabilistically, retaining the uncertainty of each target that constitutes the roll-up values.

Roll-ups to prospect level are performed automatically when a prospect's target resource is updated. Roll-ups of a portfolio, play or a list of selected prospects is performed on demand, using the Roll-up function in PLDB.

Exploration to Development Life-cycle

PLDB supports both Exploration and Development phases of an asset's life-cycle.

The following prescribes a 'simplified' scenario from pre-discovery to development.

Exploration

A new permit has been acquired, and a play based exploration study has yielded several potential plays within the permit area. These plays are entered in PLDB, together with their risk assessment of charge, source and seal capability.

Specific traps are identified using the available sparse seismic, and prospects are entered into PLDB, capturing the geographical location via a prospect polygon. Prospect targets are also created, reflecting the play types identified. A resource estimate is initially entered, based on wide ranging input parameters, and conservative risk assessments. As further technical work occurs, and uncertainty in aspects of the resource estimate reduce, and resource volumes are refined. Additional seismic acquired across the prospect may reduce the uncertainty in pool area, and data acquired from an adjacent permit may provide more certainty in reservoir properties. PLDB  is used to capture the details of this technical work, for current and future reference.

Each time the resource volumes are re-estimated, PLDB stores the results. The changes to resource volumes can therefore be tracked over time as the prospect 'evolves'.

A wildcat drilling opportunity location is proposed, and captured in PLDB, and links to the targets that it is expected to penetrate.

Our prospect is put forward in the annual budget, and accepted as an approved Exploration project.

The wildcat well, which aims to test for the presence of hydrocarbon in two primary targets, is drilled. One target successfully discovers oil, but the second target proves dry. A post drill analysis is performed in PLDB, including a critical risk analysis on the 'failed' target, This critical risk analysis indicates the seal risk was underestimated, and this valuable insight is captured for future reference.

Appraisal and Development

The discovered resource is deemed economically viable and the target transitions to a development phase. A resource estimate is refined for the target, and categorised as a Contingent resource. Further appraisal wells are proposed to delineate the 'field' and thesde are entered as drilling opportunities in PLDB. Following each appraisal drill, the resource estimates are refined as the uncertainty of the resource volume decreases.

A field development plan is produced, including proposed infrastructure. An initial 5 well development program is approved, and these drilling opportunities are entered into PLDB, together with estimates of each well's recoverable volume. Each well allows for a portion of the previously categorised contingent resource to be converted to a reserve. However, the 5 well programme is not sufficient to produce all the recoverable volume, and therefore the resource maintains a contingent resource.

After the first 2 development wells are drilled, the post drill results are captured in PLDB. Analysis indicates the expected performance of each development well is consistent with the field development plan.

One year on, and with all 5 development wells drilled, a field review is undertaken, which includes the estimation of the ultimate recovery from each well. It is clear that the drilled wells will not drain all the estimated recoverable resource, and further wells are proposed to maximise the recoverable resource volume. The field review process is captured in PLDB, together with the extra wells and their expected recoverable resource. The remaining contingent resource is also addressed by the additional development wells.

An annual field review, captured in PLDB, provides visibility of the field's performance, and clarity on the development opportunities and remaining undeveloped resources.

Resource Classification

PLDB allows for the classification of resource volumes, as either Prospective, Discovered, Contingent, or Reserve.

A prospective resource is yet to be proven.

A discovered resource is the total proven resource, which may contain reserves, contingent and unrecoverable resources.

A contingent resource is a proven resource, but something needs to occur before it can be recovered. This 'something' can be an approved development plan, infrastructure required to deliver and process the hydrocarbon, or a market in which to sell the product. It may also be a technology requirement that needs to occur to make the production of the resource possible.

A reserve is defined as an economically viable recoverable resource, that has sufficient projects defined to deliver the product to a market, and customers to purchase the product.

In PLDB, a target transitions from an Exploration phase, where all resource volumes are classified as Prospective, to an Appraisal and Development phase following a discovery. A target that reaches the Appraisal and Development phase may contain resources in one or all resource classifications.

Prospect Target vs Drilling Opportunity Target Resources

As indicated in the above Explroation-Development scenario, PLDB allows for the estimation of resource volumes for both prospect targets and drilling opportunity targets. It is important to understand the difference between the 2 resource estimates.

A prospect target resource volume is the volume of hydrocarbon estimated to be present in the target.

A drilling opportunity target resource volume is the volume of hydrocarbon expected to be produced by the drilling opportunity, in other words, its ultimate recovery. A qualitative comparison of the total ultimate recoveries of the defined drilling opportunity targets with the prospect targets' recoverable volume, will indicate whether there is sufficient opportunities defined to fully exploit the resource. If there are insufficient opportunities defined, the technically a portion of the prospect target's recoverable volume should remain categorised as contingent.

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